L2 regularized linear regression solved using a closed-form solution. The addition of regularization, controlled by the alpha hyper-parameter, makes Ridge less likely to overfit the training data than ordinary least squares (OLS).
Data Type Compatibility: Continuous
|1||alpha||1.0||float||The strength of the L2 regularization penalty.|
use Rubix\ML\Regressors\Ridge; $estimator = new Ridge(2.0);
Return the weights of features in the decision function.
public coefficients() : array|null
Return the bias added to the decision function.
public bias() : float|null